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اثر افزایش دما بر روند تغییرات ماهانه سطح پوشش، عمق و آب معادل برف و تهدید منابع شرب و کشاورزی؛ مطالعۀ موردی (استان همدان) | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| دوره 24، شماره 93، تیر 1403، صفحه 78-61 اصل مقاله (2.17 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/idser.2024.365660.1580 | ||
| نویسندگان | ||
| علی افروزی1؛ فرشید تاران* 2 | ||
| 1دکترای آبیاری و زهکشی، دانشگاه بوعلی سینا، همدان | ||
| 2استادیار موسسه تحقیقات فنی و مهندسی کشاورزی؛ سازمان تحقیقات، آموزش و ترویج کشاورزی؛ کرج، ایران | ||
| چکیده | ||
| برف و آب حاصل از ذوب آن یکی از منابع تامین آب کشاورزی در مناطق کوهستانی مانند استان همدان است. در این مطالعه، روند زمانی بارش برف در این استان در دوره 1982 تا 2023 بررسی شد. برای این منظور، دادههای سطح پوشش برف (SC)، آب معادل برف (SWE) و عمق برف (SDepth) برای ماههای مختلف با استفاده از تصویرهای ماهوارهای حاصل از محصول FLDAS استخراج شد. نتایج نشان داد که با افزایش 3/33 درجه سانتیگراد در متوسط دما در این دوره، SC با کاهشی برابر 90/13 درصد مواجه شده است و سالهای 1982 و 2021 به ترتیب با مجموع 41043/04 و 4048/12 کیلومتر مربع بیشترین و کمترین SC را داشته اند. به طور میانگین، پوشش برف در ماه های می تا اکتبر وجود نداشته و در آوریل و نوامبر ناچیز (به ترتیب 170/10 و 286/72 کیلومتر مربع) بوده است. ژانویه بیشترین SC را با 5182/22 کیلومتر مربع داشته و در فوریه، مارس و دسامبر نیز پوشش برف مشاهده شده که همزمان با اواخر پاییز تا اوایل بهار ایران است. نتایج آزمونهای من-کندال و شیب تایل-سن نشان داد که روند SC، SWE و SDepth در ژانویه، فوریه و مارس کاهشی و معنی دار بوده است. در هر چهار ماه ژانویه، فوریه، مارس و دسامبر، روند دمای هوا افزایشی و روند بارش کاهشی بوده است. در ژانویه و دسامبر، SC به ترتیب تندترین و ملایم ترین شیب کاهشی را داشته است. مقادیر ضریب پیرسون بیانگر همبستگی معکوس و معنی دار (در سطح 0/01) SC، SWE و SDepth با دماست. در کل، همبستگی این پارامترها با دما در مقایسه با بارش بیشتر بوده است، به ویژه در فوریه و مارس که دمای بیشتری نسبت به دسامبر و ژانویه داشته اند. بنابراین، با توجه به تغییر اقلیم و افزایش دمای جهانی و نقش مهم برف در تامین آب استان همدان و تاثیرپذیری زیاد آن از دما، عواملی مانند مدیریت، برنامه ریزی و زمان بندی استفاده از منابع آب این استان ضروری است. | ||
| کلیدواژه‌ها | ||
| تصاویر ماهواره ای؛ چگالی برف؛ شیب تایل-سن؛ من-کندال؛ همبستگی پیرسون | ||
| عنوان مقاله [English] | ||
| The Impact of Temperature Increase on Monthly Trends in Snow Cover, Depth, and Water Equivalent, and the Threats to Drinking and Agricultural Resources; A Case Study of Hamadan Province | ||
| نویسندگان [English] | ||
| Ali Afruzi1؛ Farshid Taran2 | ||
| 1PhD in irrigation and drainage,, Bu Ali Sina University, Hamedan. | ||
| 2Assistant Professor; Agricultural Engineering Research Institute (AERI); Agricultural Research, Education and Extension Organization (AREEO); Karaj; Iran | ||
| چکیده [English] | ||
| Extended Abstract Introduction Snow is an important source for water supply in the agricultural sector, electricity production, groundwater reserves, and rivers. This natural resource is important since it stores water in winter with low demand and releases it in hot seasons with high demand. Melted snow flow can be very useful in low-water seasons. In arid and semi-arid regions, snow is considered a basic source of fresh water. Significant spatiotemporal changes in distribution of snow on the scale of a basin can be important in determining the time and amount of snow melting in spring. These changes can increase the probability of drought and runoff. Therefore, snow cover plays an important role in the study of hydrological processes and climate changes. Snow cover is sensitive to temperature and environmental changes and can be a good indicator for local and global climate changes. The lack of sufficient and correct information about snow reserves can lead to inappropriate use of water resulting from snow melting and, as a result, irreparable damages. Therefore, measuring the surface covered by snow and its water equivalent, along with other information such as snow density, especially in areas where snow accounts for a large share of precipitation, is essential for resource planning and management. However, it is not possible to measure it in many areas due to harsh environmental conditions. Also, the data measured at one point cannot be generalized to a wide area of a basin. Thus, the use of satellite images can be considered as one of the methods of investigating spatial and temporal changes in distribution of snow in a region. The use of these images is much more economical and efficient, compared to the point data of ground stations, due to the high coverage and the ability to take pictures of an area at different times with high accuracy. Hamadan is one of the provinces with a lot of snowfall in Iran, whose economy is largely dependent on agriculture, and snowfall plays a significant role in supplying the water needed for agriculture and drinking. However, there is no research on the temporal changes of snow cover in this province and its relation with changes in important climatic parameters such as temperature and precipitation. Considering the effect of climate change on changes in snowfall over time, the aim of this study was to investigate the temporal distribution of snow cover in the province of Hamedan during 1982-2023. For this purpose, the data of snow cover, snow depth and snow water equivalent in different months of the year were estimated using satellite images obtained from the FLDAS product. Then, the increasing or decreasing trend of these data was determined. Finally, their correlation with meteorological parameters of temperature and precipitation was obtained. Materials and methods To obtain the monthly values of snow cover (SC), snow water equivalent (SWE) and snow depth (SDepth) from January 1982 to December 2023, the FLDAS (Famine Early Warning Systems Network—FEWS NET—Land Data Assimilation System) product from the Noah 3.6.1 Land Surface model was used. This product is at latitude 90° to -60° and longitude 180° to -180° with a spatial resolution of 0.1°×0.1° in netCDF format and its images are available monthly from January 1982 until now. The non-parametric Mann-Kendall test was used to investigate the increasing, decreasing or constant trend of the data during specific time intervals in the period. The Theil-Sen slope was used to calculate the slope of the trends. The correlation of the temporal distribution of snow cover with the two meteorological parameters of temperature and precipitation was determined using the Pearson's method. Results and discussion The results showed that the year 1982 with a total of 41043.04 km2 had the highest and the year 2021 with a total of 4048.12 km2 had the lowest SC. These data indicated a 90.13 percent decrease in SC in 42 years, which is understandable considering the 35.08 percent increase in the mean temperature (from 9.49 to 12.82 ºC) in this period. The monthly average of SC in this period showed that there was no snow cover from May to October, and in April and November it was very insignificant and negligible compared to in January, February, March and December. Among these four months, January had the highest average SC. According to the Mann-Kendall test, the trends of SC, SWE and SDepth was decreasing in all the four months of January, February, March and December. Temperature and precipitation values had increasing and decreasing trends, respectively. According to the Theil-Sen slope test, SC had the steepest and gentlest decreasing slope in January and December, respectively. In the case of SWE and SDepth, the steepest decreasing slope was related to February, and the gentlest decreasing slope occurred in December. The increase in temperature had the gentlest slope in the two consecutive months of December and January, and the steepest slope in the two consecutive months of February and March. The steepest and gentlest slope of decrease in precipitation occurred in March and February, respectively. The Pearson's correlation coefficient values indicated that SC, SWE and SDepth had inverse correlation with temperature and direct correlation with precipitation. Overall, SC, SWE, and SDepth were more correlated with temperature than with precipitation, especially in February and March when temperature was higher than in December and January. Conclusion In this study, using the satellite images obtained from the FLDAS product, the data of snow cover, snow water equivalent and snow depth were obtained in the province of Hamedan for the 42-year period of 1982-2023. The results showed a significant decrease in the snow cover during this period, which was expected due to the climate change and temperature increase. The snow cover, snow water equivalent and snow depth had decreasing trends. The trends of the two climatic parameters, temperature and precipitation, were increasing and decreasing, respectively. The steepest and gentlest slope of the decrease in snow cover occurred in January and December, respectively. There was an inverse correlation of snow cover, snow water equivalent and snow depth with temperature, and a direct correlation with precipitation. In general, the correlation of snowfall parameters with temperature, especially in warmer months, was more than their correlation with precipitation. | ||
| کلیدواژه‌ها [English] | ||
| Mann-Kendall, Pearson correlation, Satellite images, Snow density, Theil-Sen slope | ||
| مراجع | ||
|
Barnett, T., Adam, J. and Lettenmaier, D. 2005. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature, 438: 303-309.
Chen, W., Ding, J., Wang, J., Zhang, J. and Zhang, Zh. 2020. Temporal and spatial variability in snow cover over the Xinjiang Uygur Autonomous Region, China, from 2001 to 2015. Peer J, 8: e8861.
Dariane, A.F., Khoramian, A. and Santi, E. 2017. Investigating spatiotemporal snow cover variability via cloud-free MODIS snow cover product in Central Alborz Region. Remote Sensing of Environment, 202: 152-165.
Ghasemifar, E., Rezaeyi, M. and Eysvand Zibaie, F. 2015. Spatio-temporal analysis of snow cover in Iran based on topographic characteristics. Journal of Applied Research in Geographical Sciences, 15 (39): 117-137. (In Persian)
Guo, R.Y., Ji, X., Liu, Ch.Y., Liu, Ch., Jiang, W. and Yang, L.Y. 2022. Spatiotemporal variation of snow cover and its relationship with temperature and precipitation in the Yarlung Tsangpo-Brahmaputra River Basin. Journal of Mountain Science, 19 (7): 1901-1918.
Kendall, M.G. 1975. Rank Correlation Methods, 4th edition. Charles Griffin, London.
Khali, H., Pandey, P., Ali, Sh.N. and Champatiray, P.K. 2002. MODIS Observed Spatiotemporal Variation of Snow Cover in Zanskar Valley, North-Western Himalaya. Frontiers in Water, 4: 853146.
Kuter, S., Akyurek, Z. and Weber, G.W. 2018. Retrieval of fractional snow covered area from MODIS data by multivariate adaptive regression splines. Remote Sensing of Environment, 205: 236-252.
Lee, S., Klein, A.G. and Over, Th.M. 2005. A comparison of MODIS and NOHRSC snow-cover products for simulating streamflow using the Snowmelt Runoff Model. Hydrological Processes, 19 (15): 2951-2972.
Lopez, P., Sirguey, P., Arnaud, Y., Pouyaud, B. and Chevallier, P. 2008. Snow cover monitoring in the Northern Patagonia Icefield using MODIS satellite images (2000-2006). Global and Planetary Change, 61 (3-4): 103-116.
Mann, H.B. 1945. Non-parametric tests against trend, Econometrica, 13 (3): 245-259.
Marchane, A., Jarlan, L., Hanich, L., Boudhar, A Gascoin, S., Tavernier, A., Filali, N., Page, M.L., Hagolle, O. and Berjamy, B. 2015. Assessment of daily MODIS snow cover products to monitor snow cover dynamics over the Moroccan Atlas mountain range. Remote Sensing of Environment, 160: 72-86.
Maryanaji, Z. and Abbasi, H. 2016. Study of changes in snowfall for winter tourism planning in the Alvand mountains in Hamedan. Journal of Heritage and Tourism, 1 (3): 37-52. (In Persian)
McNally, A.L. NASA/GSFC/HSL. 2018. FLDAS Noah Land Surface Model L4 Global Monthly 0.1 x 0.1 degree (MERRA-2 and CHIRPS), Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [3/24/2024].
Salahi, B. and Nakhostinrouhi, M. 2019. Spatial and temporal monitoring of snow cover using NOAA-AVHRR Images (Case study: Balighlu Chay Watershed). Iranian Water Research Journal, 12 (3): 89-97. (In Persian)
Selkowitz, D.J. and Forster, R.R. 2016. An automated approach for mapping persistent ice and snow cover over high latitude regions. Remote Sensing, 8 (1): 16.
Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63 (324): 1379-1389.
She, J., Zhang, Y., Li, X. and Feng, X. 2015. Spatial and temporal characteristics of snow cover in the Tizinafu watershed of the western Kunlun Mountains. Remote Sensing, 7 (4): 3426-3445.
Shrestha, M., Wang, L., Koike, T., Tsutsui, H., Xue, Y. Hirabayashi, Y. 2014. Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing data. Hydrology and Earth System Sciences, 18 (2): 747-761.
Smith, T. and Bookhagen, B. 2018. Changes in seasonal snow water equivalent distribution in High Mountain Asia (1987 to 2009). Science Advances, 4 (1): e1701550.
Solaimani, K., Darvishi, Sh., Shokrian, F. and Rashidpour, M. 2018. Monitoring temporal-spatial variations of snow cover using MODIS images (Case Study: Kurdistan Province). Iranian Remote Sensing & GIS, 10 (3): 77-104. (In Persian)
Tekeli, A.E., Akyurek, Z., Sorman, A.A., Sensoy, A. and Sorman, A.U. 2005. Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey. Remote Sensing of Environment, 97 (2): 216-230.
Theil, H. 1950. A rank-invariant method of linear and polynomial regression analysis." I, II, III. Nederlands Akad. Wetensch. Proc. Ser. A, 53, 386-392, 521-525, 1397-1412.
Wang, L., Sharp, M., Brown, R., Derksen, Ch. and Rivard, B. 2005. Evaluation of spring snow covered area depletion in the Canadian Arctic from NOAA snow charts. Remote Sensing of Environment, 95 (4): 453-463.
Xin, J., Ke, C., Xu, Y. and Li, X. 2015. Spatial and temporal variations of snow cover in the Loess Plateau, China. International Journal of Climatology, 35 (8):1721-1731.
Yang, J., Jiang, L., Shi, J., Wu, Sh., Sun, R. and Yang, H. 2014. Monitoring snow cover using Chinese meteorological satellite data over China. Remote Sensing of Environment, 143: 192-203.
Ye, L. and Grimm, N.B. 2013. Modelling potential impacts of climate change on water and nitrate export from a mid-sized, semiarid watershed in the US Southwest. Climatic Change, 120 (1-2): 419-431.
Zhang, T., 2005. Influence of the seasonal snow cover on the ground thermal regime: an overview. Reviews of Geophysics, 43 (4): 1-23.
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